Learning Humanoid Navigation from Human Data
We present EgoNav, a system that enables a humanoid robot to traverse diverse, unseen environments by learning entirely from 5 hours of human walking data, with no robot data or finetuning. A diffusion model predicts distributions of plausible future trajectories conditioned on past trajectory, a 360 deg visual memory fusing color, depth, and semantics, and video features from a frozen DINOv3 backbone that capture appearance cues invisible to depth sensors. A hybrid sampling scheme achieves real-time inference in 10 denoising steps, and a receding-horizon controller selects paths from the predicted distribution. We validate EgoNav through offline evaluations, where it outperforms baseline...
| Authors | Weizhuo Wang, Yanjie Ze, C. Karen Liu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 1 April 2026 |
| Last updated at source | 1 April 2026 |
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